AmosJoseph's repositories
multifidelitymodeling
Multi-fidelity surrogate modeling
AutoGPT
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
BWMa688
Config files for my GitHub profile.
chapyter
Chapyter: ChatGPT Code Interpreter in Jupyter Notebooks
clash_for_windows_pkg
A Windows/macOS GUI based on Clash
DISCOVER
Deep identification of symbolic open-form PDEs via enhanced reinforcement-learning
Ejector_GPR
OpenAccess sharing of data and GPR code based on research by Knut Emil Ringstad. Article: NA
FastChat
An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and FastChat-T5.
feloopy
FelooPy: An integrated optimization environment for AutoOR in Python
GEKKO
GEKKO Python for Machine Learning and Dynamic Optimization
gpt-pilot
Dev tool that writes scalable apps from scratch while the developer oversees the implementation
interactive-gp-visualization
Interactive visualization of Gaussian processes
llama
Inference code for LLaMA models
PhySO
Physical Symbolic Optimization
PyTorch-BayesianCNN
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
qio-samples
Samples for using optimization solvers through Azure Quantum.
robust_mobo
Code for "Robust Multi-Objective Bayesian Optimization Under Input Noise"
ScenarioRBDO
A set of methods and proprieties to perform reliability-based-design-optimization by Scenario theory. Scenario optimization makes direct use of the available data (the uncertain parameters delta) thereby eliminating the need for estimating the distribution of the uncertain parameters
sciann-applications
A place to share problems solved with SciANN
SISSO
A data-driven method combining symbolic regression and compressed sensing for accurate & interpretable models.
UQpy
UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.
VENI-VINDy-VICI
An interpretable data-driven framework for building generative reduced order models with embedded uncertainty quantification